With the recent advances in targeted therapeutics and the progress in new approaches in target identification, novel anticancer agents with new mechanisms of action are under intensive investigation. Quinoxalinehydrazines represent a novel class of compounds with excellent potency in a panel of cancer cell lines.
Previously, we discovered a salicylhydrazide (SC) class of compounds with remarkable potency against several human cancer cell lines. SC141, a prototype of a series of quinoxalinhydrazides, showed in vivo efficacy in mice xenograft models of human breast cancer. We also reported structure-activity relationship studies by preparing a series of compounds using a one-step coupling of 7-fluoro-4-chloropyrrolo[1,2-a]quinoxalines with pyrazin-2-carbohydrazide.1 The newly synthesized compounds were tested against four cancer cell lines using MTT and colony formation assays. The substitution at the 2-carbohydrazide moiety had a dramatic effect on the activity of the drugs. A representative compound, SC161, showed higher activity than other analogues.1 We therefore explored SC161 as a lead molecule to design novel anticancer agents with greater potency and better pharmacokinetic properties.
Recently, we presented a 10 ns molecular dynamics (MD) simulation of SC161 followed by a seven feature pharmacophore model development according to the most probable conformation to capture the high probabilistic feature orientation. Its application to database mining successfully identified several potent agents, and some of the compounds showed comparable activity profiles with SC161.
The pharmacophore concept has been widely accepted as an efficient tool for use in combination with other technologies in drug design and optimization.2-6 Pharmacophore is defined as a three-dimensional (3D) arrangement of chemical features (functional groups), which is responsible for the compound to be active against an enzyme or receptor. A pharmacophore derived on the basis of a bonafide lead compound can be used as a search query to retrieve compounds with diverse structural scaffolds from a database of compounds.7 In general, pharmacophore models can be generated either using a set of known inhibitors of an enzyme or the active site of the enzyme. Analogue-based pharmacophore models are generated by utilizing a set of known inhibitors. The use of pharmacophore models as search queries are expected to retrieve novel compounds that contain desired pharmacophoric features with diverse structural and chemical features. These compounds are then expected to bind the drug target in a similar manner as the model compounds and to exert a similar biological response. This provides an extensive chemical space for lead identification and optimization. Structure-based pharmacophore models are generated based on key chemical features in the active site of an enzyme. The use of models as search queries is expected to retrieve compounds containing complementary pharmacophoric features and shape. When the structural information of the receptor is unknown, the analogue-based approach is effective to define a pharmacophore model. A receptor-based approach requires detailed and accurate information on the key features of the enzyme active site that are involved in drug binding.7 Both pharmacophore model approaches have been successfully applied to identify novel inhibitors specific to certain drug targets.8-10 